Check Umis in Subsampled Libs
Kent Riemondy RBI
March 22, 2017
libs <- list("standard", "biotinylated", "phosphorothioate")
original <- "original"
cells <- list(
standard = c(
"Cell_4Bone_63_70",
"Cell_4Bone_27_61",
"Cell_4Bone_34_48",
"Cell_1Tumor_0_55",
"Cell_1Tumor_7_50",
"Cell_4Bone_30_23",
"Cell_4Bone_61_22",
"Cell_4Bone_66_9",
"Cell_3Brain_47_38",
"Cell_3Brain_14_46"
),
biotin = c(
"Cell_4Bone_63_70",
"Cell_4Bone_27_61",
"Cell_4Bone_34_48",
"Cell_1Tumor_0_55",
"Cell_1Tumor_7_50",
"Cell_3Brain_47_38",
"Cell_3Brain_14_46",
"Cell_4Bone_28_54",
"Cell_4Bone_26_20",
"Cell_4Bone_60_43"
),
phosphoro = c(
"Cell_1Tumor_7_50",
"Cell_3Brain_47_38",
"Cell_4Bone_28_54",
"Cell_4Bone_26_20",
"Cell_4Bone_60_43"
)
)
bc_metadat <- "../../data/original/fastq/original/well_data_barcode_keys.txt"
bc_metadat <- read_tsv(bc_metadat, col_names = c("cell", "bc"))
get_counts <- function(count_dat){
res <- count_dat %>%
group_by(cell) %>%
summarize(total = sum(counts))
}
tidy_matrix <- function(x){
tidyr::gather(x, cell, counts, -GENE)
}
mats <- map(libs, ~read_tsv(paste0("../../data/", .x , "/dgematrix/dge_matrix.txt")))
names(mats) <- libs
mats <- map(mats, ~tidy_matrix(.x))
summary_dat <- map(mats, ~get_counts(.x))
summary_dat <- map2(summary_dat, cells, ~mutate(.x, selected_cells = cell %in% .y))
summary_dat <- bind_rows(summary_dat, .id = "library")
og_dat <- map(original, ~read_tsv(paste0("../../data/", .x , "/dgematrix/dge_matrix.txt")))
names(og_dat) <- original
og_dat <- map(og_dat, ~tidy_matrix(.x))
og_dat_summary <- map(og_dat, ~get_counts(.x))
#og_dat_summary <- map2(og_dat_summary, cells, ~mutate(.x, selected_cells = cell %in% .y))
og_dat_summary <- bind_rows(og_dat_summary)
dat_merged <- inner_join(summary_dat, og_dat_summary, by = "cell")
dat <- dplyr::rename(dat_merged,
total = total.x,
original_total = total.y)
dat <- dat %>% filter(cell != "Cell_unmatched")
dat <- group_by(dat, library) %>%
mutate(lib_total = sum(total),
lib_orig_total = sum(original_total),
norm_total = 1e6 * (total / lib_total),
norm_original_total = 1e6 * (original_total / lib_orig_total)) %>%
select(-lib_total, -lib_orig_total) %>%
ungroup()
## log2 proportion
dat <- mutate(dat,
proportion = log2(total / original_total),
norm_proportion = log2(norm_total / norm_original_total))
dat <- mutate(dat,
selected_cells = factor(selected_cells,
levels = c("TRUE", "FALSE"),
labels = c("Selected for\nreamplification",
"Not Selected for\nreamplification"
)),
library = factor(library,
levels = libs))color_palette = c("#1F78B4", "#bdbdbd")
global_plot_theme = theme(legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12),
strip.text = element_text(size = 10))ggplot(dat, aes(original_total / 1e3, total / 1e3, colour = selected_cells)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
coord_fixed() +
facet_wrap(~library, nrow = 1) +
xlab("original library\nUMI count (thousands)") +
ylab("subsampled library\nUMI count (thousands)") +
# ggtitle("Raw reads associated with each cell barcode") +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 12, line_size = .5) +
global_plot_theme
ggsave("reads_per_barcode_scatterplot.pdf")
color_palette = c("#1F78B4", "#bdbdbd")
ggplot(dat, aes(norm_original_total / 1e3, norm_total / 1e3, colour = selected_cells)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
coord_fixed() +
facet_wrap(~library, nrow = 1) +
xlab("original library\nRPM (Thousands)") +
ylab("subsampled library\nRPM (Thousands)") +
#ggtitle("Normalized Raw reads associated with each cell barcode") +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 12, line_size = 0.5) +
global_plot_theme
ggsave("reads_per_barcode_scatterplot_normalized.pdf")ggplot(dat, aes(cell, proportion, colour = selected_cells)) +
geom_point() +
ylab(expression(paste( " Log"[2], " ", frac("subsampled", "original")))) +
scale_colour_manual(name = "Cells Selected\nfor reamplification\n", values = color_palette) +
facet_wrap(~library, nrow = 1) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12))
ggsave("reads_ratio_per_barcode_scatterplot.pdf", width = 6.25, height = 5)
ggplot(dat, aes(cell, norm_proportion, colour = selected_cells)) +
geom_point() +
ylab(expression(paste( "Log"[2], " normalized ", frac("subsampled", "original")))) +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n", values = color_palette) +
facet_wrap(~library, nrow = 1) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12))
ggsave("reads_ratio_per_barcode_scatterplot_normalized.pdf", width = 6.25, height = 5)ggplot(dat, aes(selected_cells,
proportion, fill = selected_cells)) +
geom_boxplot() +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " ", frac("subsampled", "original")))) +
scale_fill_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("reads_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)
ggplot(dat, aes(selected_cells,
norm_proportion, fill = selected_cells)) +
geom_boxplot() +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " normalized ", frac("subsampled", "original")))) +
scale_fill_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("reads_ratio_per_barcode_boxplot_normalized.pdf", width = 6, height = 5)dat<- group_by(dat, library) %>%
mutate(total_new = sum(total),
total_old = sum(original_total))
dat_group <- group_by(dat, library, selected_cells) %>%
summarize(total_new = sum(total) / unique(total_new),
total_old = sum(original_total) / unique(total_old)) %>%
gather(lib, percent_lib, -library, -selected_cells ) %>%
mutate(lib = factor(lib, levels = c("total_old", "total_new"),
labels = c("original\nlibrary", "subsampled\nlibrary")))
ggplot(dat_group, aes(lib, percent_lib, fill = relevel(selected_cells, "Selected for\nreamplification"))) +
geom_bar(stat = "identity") +
ylab("Fraction of\n Reads Assigned") +
scale_fill_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
facet_wrap(~library) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 16)
)
ggsave("proportion_per_barcode_barplot.pdf", width = 7, height = 7)
dat_group %>% rename(Method = library) %>%
summarize(
`Targeted Library Fold-Enrichment` = (nth(percent_lib, 1) / nth(percent_lib, 3))) %>%
knitr::kable()| Method | Targeted Library Fold-Enrichment |
|---|---|
| standard | 5.239036 |
| biotinylated | 4.969125 |
| phosphorothioate | 4.939264 |
mouse vs. human
get_reads_count <- function(gene_dat, cells, libs){
gene_dat <- map(gene_dat, ~colSums(.x) )
gene_dat <- map(gene_dat, ~data_frame(cell = names(.x),
total = .x))
gene_dat <- map2(gene_dat, cells,
~mutate(.x, selected_cells = cell %in% .y))
names(gene_dat) <- libs
gene_dat
}
bind_data_calc_read_fc <- function(new_dat, old_dat, libs){
res <- inner_join(new_dat, old_dat, by = "cell")
#### remove total and unmatched counts
res <- res %>% dplyr::filter(!grepl("unmatched|total", cell))
## log2 proportion
res <- group_by(res, library) %>%
mutate(lib_total = sum(total),
lib_orig_total = sum(original_total),
norm_total = 1e6 * (total / lib_total),
norm_original_total = 1e6 * (original_total / lib_orig_total)) %>%
select(-lib_total, -lib_orig_total) %>%
ungroup()
## log2 proportion
res <- mutate(res,
proportion = log2(total / original_total),
norm_proportion = log2(norm_total / norm_original_total))
res <- mutate(res,
selected_cells = factor(selected_cells,
levels = c("TRUE", "FALSE"),
labels = c("Selected for\nreamplification",
"Not Selected for\nreamplification")),
library = factor(library,
levels = libs))
res
}
get_read_summary <- function(new_dat, old_dat, libs, original) {
sub_sampled_reads <- get_reads_count(new_dat, cells, libs)
sub_sampled_reads <- bind_rows(sub_sampled_reads, .id = "library")
og_sub_sampled_reads <- get_reads_count(list(old_dat), cells, list(original))
og_sub_sampled_reads <- rename(og_sub_sampled_reads[[1]], original_total = total) %>%
select(-selected_cells)
res <- bind_data_calc_read_fc(sub_sampled_reads, og_sub_sampled_reads, libs)
res
}
gene_dat <- map(libs, ~read.table(paste0("../../data/", .x , "/dgematrix/dge_matrix.txt"),
row.names = 1, header = T))
names(gene_dat) <- libs
og_gene_dat <- read.table("../../data/original/dgematrix/dge_matrix.txt",
row.names = 1, header = T)
human_read_dat <- map(gene_dat, ~.x[grepl("^hg38::", rownames(.x)), ])
og_human_read_dat <- og_gene_dat[grepl("^hg38::", rownames(og_gene_dat)), ]
mouse_read_dat <- map(gene_dat, ~.x[grepl("^mm38::", rownames(.x)), ])
og_mouse_read_dat <- og_gene_dat[grepl("^mm38::", rownames(og_gene_dat)), ]
sub_sampled_reads_human <- get_read_summary(human_read_dat, og_human_read_dat, libs, original)
sub_sampled_reads_mouse <- get_read_summary(mouse_read_dat, og_mouse_read_dat, libs, original)
combined_dat <- inner_join(sub_sampled_reads_human, sub_sampled_reads_mouse,
by = c("cell", "selected_cells", "library")) %>%
select(library, cell, selected_cells, total.x, total.y, original_total.x, original_total.y) %>%
rename(total_human = total.x,
total_mouse = total.y,
original_total_human = original_total.x,
original_total_mouse = original_total.y) %>%
mutate(purity = total_human / (total_mouse + total_human),
original_purity = original_total_human / (original_total_human + original_total_mouse),
`% human reads > 90%` = purity > .90)
subsampled_cells <- combined_dat %>%
filter(selected_cells == "Selected for\nreamplification") %>%
select(cell) %>% unique() %>% unlist()
og_dat_to_add <- combined_dat %>%
select(cell, selected_cells, original_total_human, original_total_mouse) %>%
mutate(total_human = original_total_human,
total_mouse = original_total_mouse,
selected_cells = ifelse(cell %in% subsampled_cells, "Selected for\nreamplification", "Not Selected for\nreamplification"),
purity = total_human / (total_mouse + total_human),
`% human reads > 90%` = purity > .90,
library = "original") %>%
select(library, cell, selected_cells, total_human, total_mouse, original_total_human, original_total_mouse, purity, `% human reads > 90%`) %>%
unique() #remove the duplicates
combined_dat <- bind_rows(combined_dat, og_dat_to_add)
combined_dat <- mutate(combined_dat,
library = factor(library,
levels = c("original", libs)))Here are the number of mapped reads compared:
ggplot(combined_dat, aes(total_human / 1e6, total_mouse / 1e6, colour = selected_cells)) +
geom_point(size = 0.5) +
coord_fixed() +
facet_wrap(~library, nrow = 2, scales = "free") +
xlab("Human reads (Millions)") +
ylab("Mouse reads (Millions)") +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12))
ggsave("mapped_reads_per_barcode_scatterplot__human_mouse.pdf", width = 7, height = 7)
ggplot(sub_sampled_reads_human, aes(norm_original_total / 1e3, norm_total / 1e3, colour = selected_cells)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
facet_wrap(~library, nrow = 1) +
xlab("original library\nRPM (Thousands)") +
ylab("subsampled library\nRPM (Thousands)") +
labs(title = "Human UMIs") +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 12, line_size = 0.5) +
global_plot_theme
ggsave("mapped_reads_per_barcode_scatterplot_normalized_human.pdf")
ggplot(sub_sampled_reads_mouse, aes(norm_original_total / 1e3, norm_total / 1e3, colour = selected_cells)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
labs(title = "Mouse UMIs") +
facet_wrap(~library, nrow = 1) +
xlab("original library\nRPM (Thousands)") +
ylab("subsampled library\nRPM (Thousands)") +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 12, line_size = 0.5) +
global_plot_theme
ggsave("mapped_reads_per_barcode_scatterplot_normalized_mouse.pdf")
ggplot(sub_sampled_reads_human, aes(cell, norm_proportion, colour = selected_cells)) +
geom_point() +
ylab(expression(paste( "Log"[2], " normalized human ", frac("subsampled", "original")))) +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n", values = color_palette) +
facet_wrap(~library, nrow = 1) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 16)
)
ggsave("mapped_reads_ratio_per_barcode_scatterplot_normalized_human.pdf", width = 7, height = 7)
ggplot(sub_sampled_reads_mouse, aes(cell, norm_proportion, colour = selected_cells)) +
geom_point() +
ylab(expression(paste( "Log"[2], " normalized mouse ", frac("subsampled", "original")))) +
ggtitle("Enrichment of cell barcodes in subsampled library") +
scale_colour_manual(name = "Cell Selected\nfor reamplification\n", values = color_palette) +
facet_wrap(~library, nrow = 1) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 16)
)
ggsave("mapped_reads_ratio_per_barcode_scatterplot_normalized_mouse.pdf", width = 7, height = 7)genes
count_detected_genes <- function(gene_dat, .min_reads = 0, cells, libs){
gene_dat <- map(gene_dat, ~dmap(.x, ~sum(.x > .min_reads ) ) %>% unlist() )
gene_dat <- map(gene_dat, ~data_frame(cell = names(.x),
n_genes = .x))
gene_dat <- map2(gene_dat, cells,
~mutate(.x, selected_cells = cell %in% .y))
names(gene_dat) <- libs
gene_dat
}
bind_data_calc_fc <- function(new_dat, old_dat){
res <- inner_join(new_dat, old_dat, by = "cell")
#### remove total and unmatched counts
res <- res %>% dplyr::filter(!grepl("unmatched|total", cell))
## log2 proportion
res <- mutate(res,
proportion = log2(n_genes / orig_n_genes))
res <- mutate(res,
selected_cells = factor(selected_cells,
levels = c("TRUE", "FALSE"),
labels = c("Selected for\nreamplification",
"Not Selected for\nreamplification")),
library = factor(library,
levels = libs))
res
}
get_gene_summary <- function(new_dat, old_dat, libs, original) {
sub_sampled_genes <- count_detected_genes(new_dat, .min_reads = 0, cells, libs)
sub_sampled_genes <- bind_rows(sub_sampled_genes, .id = "library")
og_sub_sampled_genes <- count_detected_genes(list(old_dat), .min_reads = 0, cells, list(original))
og_sub_sampled_genes <- rename(og_sub_sampled_genes[[1]], orig_n_genes = n_genes) %>%
select(-selected_cells)
res <- bind_data_calc_fc(sub_sampled_genes, og_sub_sampled_genes)
res
}
gene_dat <- map(libs, ~read.table(paste0("../../data/", .x , "/dgematrix/dge_matrix.txt"),
row.names = 1, header = T))
names(gene_dat) <- libs
og_gene_dat <- read.table("../../data/original/dgematrix/dge_matrix.txt",
row.names = 1, header = T)
human_gene_dat <- map(gene_dat, ~.x[grepl("^hg38::", rownames(.x)), ])
og_human_gene_dat <- og_gene_dat[grepl("^hg38::", rownames(og_gene_dat)), ]
mouse_gene_dat <- map(gene_dat, ~.x[grepl("^mm38::", rownames(.x)), ])
og_mouse_gene_dat <- og_gene_dat[grepl("^mm38::", rownames(og_gene_dat)), ]
sub_sampled_genes_human <- get_gene_summary(human_gene_dat, og_human_gene_dat, libs, original)
sub_sampled_genes_mouse <- get_gene_summary(mouse_gene_dat, og_mouse_gene_dat, libs, original)ggplot(sub_sampled_genes_human, aes(orig_n_genes / 1e3, n_genes / 1e3, colour = selected_cells)) +
geom_point(size = 0.5) +
coord_fixed() +
xlab("human genes detected\n original lib. (K)") +
ylab("human genes detected\n subsampled lib. (K)") +
scale_colour_manual(values = color_palette) +
facet_wrap(~library, nrow = 1) +
geom_abline(slope = 1) +
coord_equal() +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("genes_per_barcode_scatterplot_human.pdf", width = 6, height = 5)
ggplot(sub_sampled_genes_mouse, aes(orig_n_genes / 1e3, n_genes / 1e3, colour = selected_cells)) +
geom_point(size = 0.5) +
coord_fixed() +
xlab("mouse genes detected\n original lib. (K)") +
ylab("mouse genes detected\n subsampled lib. (K)") +
scale_colour_manual(values = color_palette) +
facet_wrap(~library, nrow = 1) +
geom_abline(slope = 1) +
coord_equal() +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("genes_per_barcode_scatterplot_mouse.pdf", width = 6, height = 5)ggplot(sub_sampled_genes_human, aes(cell, proportion, colour = selected_cells)) +
geom_point() +
ylab(expression(paste("genes ratio ", frac("subsampled library", "original library"), " Log2"))) +
ggtitle("Enrichment of human genes in subsampled library") +
facet_wrap(~library, nrow = 1) +
scale_colour_manual(values = color_palette) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
axis.text.x = element_blank()
)
ggsave("genes_per_barcode_enrichment_human.pdf")
ggplot(sub_sampled_genes_mouse, aes(cell, proportion, colour = selected_cells)) +
geom_point() +
ylab(expression(paste("genes ratio ", frac("subsampled library", "original library"), " Log2"))) +
ggtitle("Enrichment of mouse genes in subsampled library") +
facet_wrap(~library, nrow = 1) +
scale_colour_manual(values = color_palette) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
axis.text.x = element_blank()
)
ggsave("genes_per_barcode_enrichment_mouse.pdf")
ggplot(sub_sampled_genes_human, aes(selected_cells, proportion, colour = selected_cells)) +
geom_boxplot() +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " normalized ", frac("subsampled", "original")))) +
scale_color_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("genes_ratio_per_barcode_boxplot_human.pdf", width = 6, height = 5)
ggplot(sub_sampled_genes_mouse, aes(selected_cells, proportion, colour = selected_cells)) +
geom_boxplot() +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " normalized ", frac("subsampled", "original")))) +
scale_color_manual(name = "Cell Selected\nfor reamplification\n",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("genes_ratio_per_barcode_boxplot_mouse.pdf", width = 6, height = 5)human_resampled <- filter(sub_sampled_genes_human,
selected_cells == "Selected for\nreamplification") %>%
gather(gene_origin, gene_count, -library, -cell, -selected_cells) %>%
mutate(gene_origin = ifelse(gene_origin == "n_genes",
"subsampled", "original"))
mouse_resampled <- filter(sub_sampled_genes_mouse,
selected_cells == "Selected for\nreamplification") %>%
gather(gene_origin, gene_count, -library, -cell, -selected_cells) %>%
mutate(gene_origin = ifelse(gene_origin == "n_genes",
"subsampled", "original"))
ggplot(human_resampled, aes(cell, gene_count, fill = gene_origin)) +
geom_bar(stat = "identity", position = "dodge") +
ylab("Human\nGenes Detected") +
scale_fill_manual(values = color_palette) +
facet_wrap(~library, nrow = 1, scales = "free_x") +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
legend.title = element_blank(),
legend.position = "top",
axis.title.x = element_blank(),
axis.text.x = element_text(size = 8, angle = 90),
legend.text = element_text(size = 12)
)
ggsave("genes_per_cell_barplot_human.pdf", width = 6, height = 5)
ggplot(mouse_resampled, aes(cell, gene_count, fill = gene_origin)) +
geom_bar(stat = "identity", position = "dodge") +
ylab("Mouse\nGenes Detected") +
scale_fill_manual(values = color_palette) +
facet_wrap(~library, nrow = 1, scales = "free_x") +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
legend.title = element_blank(),
legend.position = "top",
axis.title.x = element_blank(),
axis.text.x = element_text(size = 8, angle = 90),
legend.text = element_text(size = 12)
)
ggsave("genes_per_cell_barplot_human.pdf", width = 6, height = 5)